Analysts like IDC and Deloitte estimate that up to 80% of the world’s data is unstructured text data, which makes getting valuable insights out of this type of data a huge challenge. Worse, customers can’t easily find the right answers to address their product and service-related questions that are hidden in large amounts of support documents. As a result, employees […]
Why Data Quality Problems Plague Most Organizations (and What to Do About It)
For business leaders to make informed decisions, they need high-quality data. Unfortunately, most organizations – across all industries – have Data Quality problems that are directly impacting their company’s performance. Case in point: In a recent survey conducted by my company, practitioners were asked about the issues that plague their work, how much they trust their organization’s […]
Why Data Storytelling Matters to Data Scientists
In an increasingly data-driven world, companies worldwide have transformed the way they operate. Much of this is thanks to their ability to access a volume of data that was not available in the past. However, more recently, organizations have realized that simply collecting data is not enough, with many struggling to master the language of data. […]
Data Science: How to Shift Toward More Transparency in Statistical Practice
Data Science and statistics both benefit from transparency, openness to alternative interpretations of data, and acknowledging uncertainty. The adoption of transparency is further supported by important ethical considerations like communalism, universalism, disinterestedness, and organized skepticism. Promoting transparency is possible through seven statistical procedures: Data visualization Quantifying inferential uncertainty Assessment of data preprocessing choices Reporting multiple models Involving […]
How to Start Using AI at Your Company
So you want to start using AI at your company. Now what? First, evaluate if it has an appropriate place in your company. Many organizations hire a data scientist or an entire AI team with an anticipation of a fast, massive, magical gain. Even though by now most people realize that these expectations are naive, […]
How Low-Code Is Bridging the Gap Between Data Scientists and Businesses
Coding is a foreign language for many high-level and C-suite executives who lead corporations today. However, it’s hard to find anyone who doesn’t understand the importance of technology and how our future will hinge on mastering it. Although many computer scientists argue in favor of low-code software for data scientists, there is an argument to […]
3 Strategies for Creating a Successful MLOps Environment
Disconnects between development, operations, data engineers, and data science teams might be holding your organization back from extracting value from its artificial intelligence (AI) and machine learning (ML) processes. In short, you may be missing the most essential ingredient of a successful MLOps environment: collaboration. For instance, your data scientists might be using tools like JupyterHub or […]
Why Synthetic Data Still Has a Data Quality Problem
According to Gartner, 85% of Data Science projects fail (and are predicted to do so through 2022). I suspect the failure rates are even higher, as more and more organizations today are trying to utilize the power of data to improve their services or create new revenue streams. Not having the “right” data continues to prevent […]
11 Intriguing Roles for Data Scientists in 2022
Data Science is a diverse field with an array of career and job options out there to pursue. The modern economy is dependent on data and data analysis so, naturally, data scientists are in high demand and enjoy good salary and job security prospects. With that in mind, below are 11 intriguing roles for data […]
4 Reasons Data Scientists Leave (and How to Retain Top Talent)
The demand for Data Science talent far exceeds the talent pool, making employee retention challenging. Demand is expected to remain high throughout 2022, providing ample opportunity for talent to move upwards through competing organizations. A vast amount of project familiarity and understanding is built on the backs of data scientists and is lost when they […]